scholarly journals Chemical Space Overlap with Critical Protein‐Protein Interface Residues in Commercial and Specialized Small‐Molecule Libraries

ChemMedChem ◽  
2018 ◽  
Author(s):  
Yubing Si ◽  
David Xu ◽  
Khuchtumur Bum-Erdene ◽  
Mona Ghozayel ◽  
Baocheng Yang ◽  
...  
Keyword(s):  
Small Molecule ◽  
Chemical Space ◽  
Protein Interface ◽  
Small Molecule Libraries ◽  
Interface Residues

scholarly journals PIPENN: Protein Interface Prediction with an Ensemble of Neural Nets

2021 ◽  
Author(s):  
Bas Stringer ◽  
Hans De Ferrante ◽  
Sanne Abeln ◽  
Jaap Heringa ◽  
K. Anton A. Feenstra ◽  
...  
Keyword(s):  
Small Molecules ◽  
Small Molecule ◽  
Protein Interaction ◽  
Receptor Activation ◽  
Neural Nets ◽  
Data Sets ◽  
Protein Interface ◽  
Protein Protein Interaction ◽  
Independent Test ◽  
Interface Residues

Motivation: Protein interactions play an essential role in many biological and cellular processes, such as protein—protein interaction (PPI) in signaling pathways, binding to DNA in transcription, and binding to small molecules in receptor activation or enzymatic activity. Experimental identification of protein binding interface residues is a time-consuming, costly, and challenging task. Several machine learning and other computational approaches exist which predict such interface residues. Here we explore if Deep Learning (DL) can be used effectively for this prediction task, and which learning strategies and architectures may be most efficient. We introduce seven DL architectures that are applied to eleven independent test sets, focused on the residues involved in PPI interfaces and in binding RNA/DNA and small molecule ligands. Results: We constructed a large data set dubbed BioDL, comprising protein-protein interaction data from the PDB and protein-ligand interactions (DNA, RNA and small molecules) from the BioLip database. Additionally, we reused our existing curated homo- and heteromeric PPI data sets. We performed several experiments to assess the impact of different data features, spatial forms, encoding schemes, network initializations, loss functions, regularization mechanisms, and activation functions on the performance of the predictors. Benchmarking the resulting DL models with an independent test set (ZK448) shows no single DL architecture performs best on all instances, but that an ensemble of DL architectures consistently achieves peak prediction performance. Our PIPENN's ensemble predictor outperforms current state-of-the-art sequence-based protein interface predictors on all interaction types, achieving AUCs of 0.718 (protein—protein), 0.823 (protein—nucleotide) and 0.842 (protein—small molecule) respectively. Availability: Source code and data sets at https://github.com/ibivu/

Pentaerythritol and Its Congeners: Versatile Building Blocks for the Construction of Oligomeric Bioconjugates and Small Molecule Libraries on a Solid Support

2004 ◽  
Vol 8 (11) ◽  
pp. 977-992 ◽  
Author(s):  
Johanna Katajisto ◽  
Petri Heinonen ◽  
Harri Lonnberg
Keyword(s):  
Small Molecule ◽  
Building Blocks ◽  
Solid Support ◽  
Small Molecule Libraries

scholarly journals Screening of DNA-Encoded Small Molecule Libraries inside a Living Cell

2021 ◽  
Vol 143 (7) ◽  
pp. 2751-2756
Author(s):  
Lars K. Petersen ◽  
Allan B. Christensen ◽  
Jacob Andersen ◽  
Charlotta G. Folkesson ◽  
Ole Kristensen ◽  
...  
Keyword(s):  
Small Molecule ◽  
Living Cell ◽  
Small Molecule Libraries

Towards drugging the ‘undruggable’: enhancing the scaffold diversity of synthetic small molecule screening collections using diversity-oriented synthesis

2013 ◽  
Vol 1 (1) ◽  
Author(s):  
Warren R.J.D. Galloway ◽  
David R. Spring
Keyword(s):  
Small Molecule ◽  
Structural Diversity ◽  
Library Size ◽  
Diversity Oriented Synthesis ◽  
Biological Targets ◽  
Small Molecule Screening ◽  
Chemistry Research ◽  
Large Numbers ◽  
Small Molecule Libraries ◽  
Scaffold Diversity

AbstractMedicinal chemistry research has traditionally focused upon a limited set of biological targets. Many other human disease-related targets have been termed ‘undruggable’ as they have proved largely impervious to modulation by small molecules. However, it is becoming increasingly evident that such targets can indeed be modulated; they are simply being challenged with the wrong types of molecules. Traditionally, screening libraries were composed of large numbers of structurally similar compounds. However, library size is not everything; the structural diversity of the library, which is largely dictated by the range of molecular scaffolds present, is crucial. Diversity-oriented synthesis (DOS) generates small molecule libraries with high levels of scaffold, and thus structural, diversity. Such collections should provide hits against a broad range of targets with high frequency, including ‘undruggable’ targets. Examples in the area of scaffold diversity generation taken from the author’s laboratories are given.

Carbohydrate-Derived Small-Molecule Libraries

2006 ◽  
pp. 165-184
Author(s):  
Armen M. Boldi
Keyword(s):  
Small Molecule ◽  
Small Molecule Libraries

scholarly journals Prioritizing Small Molecule as Candidates for Drug Repositioning using Machine Learning

2018 ◽  
Author(s):  
Khader Shameer ◽  
Kipp W. Johnson ◽  
Benjamin S. Glicksberg ◽  
Rachel Hodos ◽  
Ben Readhead ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Small Molecule ◽  
Chemical Space ◽  
Drug Repositioning ◽  
Chemical Properties ◽  
Support Vector ◽  
Feature Engineering ◽  
Connectivity Map ◽  
Molecular Features

ABSTRACTDrug repositioning, i.e. identifying new uses for existing drugs and research compounds, is a cost-effective drug discovery strategy that is continuing to grow in popularity. Prioritizing and identifying drugs capable of being repositioned may improve the productivity and success rate of the drug discovery cycle, especially if the drug has already proven to be safe in humans. In previous work, we have shown that drugs that have been successfully repositioned have different chemical properties than those that have not. Hence, there is an opportunity to use machine learning to prioritize drug-like molecules as candidates for future repositioning studies. We have developed a feature engineering and machine learning that leverages data from publicly available drug discovery resources: RepurposeDB and DrugBank. ChemVec is the chemoinformatics-based feature engineering strategy designed to compile molecular features representing the chemical space of all drug molecules in the study. ChemVec was trained through a variety of supervised classification algorithms (Naïve Bayes, Random Forest, Support Vector Machines and an ensemble model combining the three algorithms). Models were created using various combinations of datasets as Connectivity Map based model, DrugBank Approved compounds based model, and DrugBank full set of compounds; of which RandomForest trained using Connectivity Map based data performed the best (AUC=0.674). Briefly, our study represents a novel approach to evaluate a small molecule for drug repositioning opportunity and may further improve discovery of pleiotropic drugs, or those to treat multiple indications.

Protein Interface Residues Recognition Using Granular Computing Theory

2010 ◽  
pp. 727-734
Author(s):  
Jiaxing Cheng ◽  
Xiuquan Du ◽  
Jiehua Cheng
Keyword(s):  
Granular Computing ◽  
Protein Interface ◽  
Interface Residues
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